Protecting privacy in machine learning has attracted wide attention and spawned many approaches in terms of collaborative learning where datasets are locally used at edge devices with only small subsets of parameters shared [1]}, obfuscating the local model parameters by using random noise or differential privacy at edge devices [2]}, adopting homomorphic encryption to encrypt data samples [3]}, etc.
